Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates.

BACKGROUND: Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. METHODS: A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. RESULTS: The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. CONCLUSION: When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used.

Effect of Partial Sick Leave on Sick Leave Duration in Employees with Musculoskeletal Disorders.

Objective This study determined if partial sick leave was associated with a shorter duration of sick leave due to musculoskeletal disorders (MSD) based on routinely collected health data in Dutch sick-listed employees. Furthermore, the effect of timing of partial sick leave on sick leave duration was determined. Methods This cohort study consisted of 771 employees with partial sick leave and 198 employees with full-time sick leave who participated in an occupational health check, and had sick leave due to MSD for minimally 4 weeks and were diagnosed by an occupational physician. Multivariable linear regression models were performed to determine the effects of partial sick leave (unadjusted and adjusted for confounders and MSD diagnosis) and Kaplan-Meier curves were presented for visualization of return to work for different timings of starting partial sick leave. Furthermore, linear regression analysis were done in subsets of employees with different minimal durations of sick leave to estimate the effects of timing of partial sick leave. Results Initial results suggest that partial sick leave was associated with longer sick leave duration, also when adjusted for confounders and sick leave diagnosis. Secondary results which accounted for the timing of partial sick leave suggest that partial sick leave had no effect on the duration of sick leave. Conclusion Partial sick leave does not influence MSD sick leave duration in this study when accounting for the timing of partial sick leave.

Development of Prediction Models for Sick Leave Due to Musculoskeletal Disorders.

Purpose The aim of this study was to develop prediction models to determine the risk of sick leave due to musculoskeletal disorders (MSD) in non-sick listed employees and to compare models for short-term (i.e., 3 months) and long-term (i.e., 12 months) predictions. Methods Cohort study including 49,158 Dutch employees who participated in occupational health checks between 2009 and 2015 and sick leave data recorded during 12 months follow-up. Prediction models for MSD sick leave within 3 and 12 months after the health check were developed with logistic regression analysis using routinely assessed health check variables. The performance of the prediction models was evaluated with explained variance (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test) and discrimination (area under the receiver operating characteristic curve, AUC) measures. Results A total of 376 (0.8%) and 1193 (2.4%) employees had MSD sick leave within 3 and 12 months after the health check. The prediction models included similar predictor variables (educational level, musculoskeletal complaints, distress, supervisor social support, work-home interference, intrinsic motivation, development opportunities, and work pace). The explained variances were 7.6% and 8.8% for the model with 3 and 12 months follow-up, respectively. Both prediction models showed adequate calibration and discriminated between employees with and without MSD sick leave 3 months (AUC = 0.761; Interquartile range [IQR] 0.759-0.763) and 12 months (AUC = 0.740; IQR 0.738-0.741) after the health check. Conclusion The prediction models could be used to determine the risk of MSD sick leave in non-sick listed employees and invite them to preventive consultations with occupational health providers.

Participation in and adherence to physical exercise after completion of primary cancer treatment.

BACKGROUND: The purpose of this study was  to identify demographic, clinical, psychosocial, physical and environmental factors that are associated with participation in and adherence to a combined resistance and endurance exercise program among cancer survivors, shortly after completion of primary cancer treatment. Data from the randomized controlled Resistance and Endurance exercise After ChemoTherapy (REACT) study were used for this study. METHODS: The participants of the REACT study were randomly allocated to either a high intensity (HI) or low-to-moderate intensity (LMI) exercise program. Patients' participation rate was defined as the cancer survivors' decision to participate in the REACT study. Exercise adherence reflected participants' attendance to the scheduled exercise sessions and their compliance to the prescribed exercises. High session attendance rates were defined as attending at least 80 % of the sessions. High compliance rates were defined as performing at least of 90 % of the prescribed exercise across all sessions. Correlates of exercise adherence were studied separately for HI and LMI exercise. Demographic, clinical, and physical factors were assessed using self-reported questionnaires. Relevant clinical information was extracted from medical records. Multivariable logistic regression analyses were applied to identify correlates that were significantly associated with participation, high session attendance, high compliance with resistance and high compliance with endurance exercises. RESULTS: Participants were more likely to have higher education, be non-smokers, have lower psychological distress, higher outcome expectations, and perceive more exercise barriers than non-participants. In HI exercise, higher self-efficacy was significantly associated with high session attendance and high compliance with endurance exercises, and lower psychological distress was significantly associated with high compliance with resistance exercises. In LMI exercise, being a non-smoker was significantly associated with high compliance with resistance exercises and higher BMI was significantly associated with high compliance with resistance and endurance exercises. Furthermore, breast cancer survivors were less likely to report high compliance with resistance and endurance exercises in LMI exercise compared to survivors of other types of cancer. The discriminative ability of the multivariable models ranged from 0.62 to 0.75. CONCLUSION: Several demographic, clinical and psychosocial factors were associated with participation in and adherence to exercise among cancer survivors. Psychosocial factors were more strongly associated with adherence in HI than LMI exercise. TRIAL REGISTRATION: This study was registered at the Netherlands Trial Register [ NTR2153 ] on the 5(th) of January 2010.

Ozone Sauna Therapy (OST) and Pulsed Electromagnetic Field Therapy (PEMF) delivered via the HOCATT machine could improve endometriosis pain along with lowering serum inflammatory markers.

PROBLEM: Endometriosis, a common and challenging condition, is a pelvic inflammatory condition that causes chronic pelvic pain (CPP) and infertility. Even though standard medical therapies and surgeries can help CPP, a large percentage of women remain symptomatic following the conventional treatments. Thus, there is a need to study new non-traditional therapeutic adjuncts in this patient population to improve their quality of life. One non-traditional therapeutic agent is Ozone Sauna Therapy (OST) which has been shown to have an anti-inflammatory action, but no studies have been performed to assess the efficacy of OST in women with endometriosis suffering with CPP. Another non-traditional therapeutic agent is Pulsed Electromagnetic Field Therapy (PEMF) where one small pilot study has shown that PEMF exposure to women with endometriosis and pelvic pain showed dramatic relief in symptoms. METHOD OF STUDY: The HOCATT machine, by delivering a combination of both OST and PEMF in one machine, has been shown to improve fertility treatment outcome via in vitro fertilization (IVF) in older women by potentially improving oocyte quality. This study was conducted to assess the effect of the HOCATT machine use on the pain scale in patients struggling with CPP due to endometriosis. In the first study, eight women with endometriosis were administered transdermal and intravaginal OST + PEMF, twice a week for 3 weeks (total of 6 sessions). Once a week, the participants were asked to fill a pain scale. In the second study, 10 women were recruited in order to evaluate the changes in serum inflammatory markers following OST + PEMF exposure after 3-week period using the HOCATT machine twice a week (total of 6 sessions). RESULTS: The participants had a mean age of 39.7 ± 1.1 years. The results showed a significant improvement in pain scale following the fourth session (p < .05) and a significant drop in serum levels of the inflammatory markers CRP (p = .0438) and IL-1ß (p = .0031) and a significant increase in serum levels of IL-8 (p = .033). CONCLUSIONS: This pilot study suggests that a combination of OST and PEMF using the HOCATT machine could potentially represent potential therapeutic adjuncts for women with inflammatory disorders such as endometriosis. There is a definite need for larger cohort studies and even randomized trials in order to better assess the efficacy of OST and PEMF in women with other inflammatory disorders.

Ozone sauna therapy and pulsed electromagnetic field therapy could potentially improve outcome in women with diminished ovarian reserve undergoing assisted reproductive technology.

There are limited treatment options for women with severely diminished ovarian reserve (DOR) who experience repeatedly failed in vitro fertilization (IVF) cycles and with persistently thin endometrial lining thickness (EMT) during frozen embryo transfer cycles. Therefore, a large majority of patients resort to using donor oocytes and gestational carriers. Data from existing animal and human studies suggest that ozone sauna therapy (OST) and pulsed electromagnetic field therapy (PEMF) are emerging as potential therapeutic adjuncts for female reproduction. This study was conducted to assess the fertility outcome of OST + PEMF in vivo in patients undergoing IVF/frozen embryo transfe and the effects of OST in vitro on human granulosa cell (GC) function. Forty-four women with DOR underwent their 1st IVF cycle (Cycle 1), and then were administered transdermal and intravaginal OST + PEMF, twice a week for 3 weeks, followed by a 2nd IVF cycle (Cycle 2) using the same protocol as in Cycle 1. GCs collected from another six women who underwent egg retrieval were equally split and cultured with OST (test) or placed in room temperature (control) outside the OST chamber in the same room. The results demonstrated that Cycles 1 and 2 had no significant difference in the number of days of stimulation, baseline hormones measured, number of oocytes retrieved or peak estradiol levels. However, the number of embryos formed after OST + PEMF in Cycle 2 was significantly higher than the Cycle 1. Furthermore, EMT measured in Cycle 2 demonstrated a significant increase compared to Cycle 1 and all patients reached a satisfactory EMT of approximately 7 mm. In vitro studies demonstrated that OST led to a 5-fold significant increase in the aromatase enzyme while a significant 50% reduction was noted in the side-chain cleavage enzyme in GCs. Both OST + PEMF are known for their vasodilatory, anti-inflammatory, and antioxidant actions, which could enhance endometrial receptivity and increase the number of formed embryos without increasing the number of oocytes retrieved, suggesting an improvement in oocyte quality. Finally, ozone can alter genes involved in steroidogenesis suggesting that it could improve ovarian function.

The Economic Burden of Chromosome Translocations and the Benefits of Enhanced Screening for Cattle Breeding.

The cattle breeding industry, through both of its derivatives (dairy and beef), provides 81% of milk and 22% of meat required globally. If a breeding bull is sub-fertile, this impacts herd conception and birth rates, and it is generally accepted that having a proactive genetic screening programme can prevent further losses. Chromosome translocations are the leading genetic cause of infertility in livestock and, in cattle, this extends beyond the classical 1:29 to other Robertsonian translocations (RobTs) and to reciprocal translocations (RECTs). The incidence of both (collectively termed RTs) varies between breeds and herds; however, we estimate that RECTs are, most likely, at least twice as common as RobTs. The purpose of this study was to develop an industry economic model to estimate the financial impact of an RT event at the herd level. If we assume a conservative incidence rate of 0.4% for Rob1:29 with each one impacting the conception rate by 5%, we calculate that actively screening for and removing a Rob1:29 bull could benefit an impacted herd by GBP 2.3 million (approx. USD 2.8 million) over six years. A recently updated screening protocol developed in our lab for all RTs, however (with a projected combined incidence of 1.2%, impacting conception rates by 10%), could benefit an impacted herd by GBP 7.2 million (nearly USD 9 million) for each RT found. For an industry worth USD 827.4 billion (dairy) and USD 467.7 billion (beef), expanding knowledge on incidence and further dissection of the potential costs (financial and environmental) from RTs is essential to prevent further losses.

Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain.

OBJECTIVE: The aim of this study was to develop a prediction model for the prognosis of sick leave due to low back pain (LBP). METHODS: This is a cohort study with 103 employees sick-listed due to non-specific LBP and spinal disc herniation. A prediction model was developed based on questionnaire data and registered sick leave data with follow up of 180 days. RESULTS: At follow up 31 (30.1%) employees were still sick-listed due to LBP. Forward selection procedure resulted in a model with: catastrophizing, musculoskeletal work load, and disability. The explained variance was 27.3%, calibration was adequate and discrimination was fair with area under the ROC-curve (AUC) = 0.761 (interquartile range [IQR]: 0.755-0.770). CONCLUSION: The prediction model of this study can adequately predict LBP sick leave after 180 days and could be used for employees sick listed due LBP.

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error.

The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.

Prediction models to identify workers at risk of sick leave due to low-back pain in the Dutch construction industry.

Objective The aim of this study was to develop a prediction model based on variables measured in occupational health checks to identify non-sick listed workers at risk of sick leave due to non-specific low-back pain (LBP). Methods This cohort study comprised manual (N=22 648) and non-manual (N=9735) construction workers who participated in occupational health checks between 2010 and 2013. Occupational health check variables were used as potential predictors and LBP sick leave was recorded during 1-year follow-up. The prediction model was developed with logistic regression analysis among the manual construction workers and validated in non-manual construction workers. The performance of the prediction model was evaluated with explained variances (Nagelkerke's R-square), calibration (Hosmer-Lemeshow test), and discrimination (area under the receiver operating curve, AUC) measures. Results During follow-up, 178 (0.79%) manual and 17 (0.17%) non-manual construction workers reported LBP sick leave. Backward selection resulted in a model with pain/stiffness in the back, physician-diagnosed musculoskeletal disorders/injuries, postural physical demands, feeling healthy, vitality, and organization of work as predictor variables. The Nagelkerke's R-square was 3.6%; calibration was adequate, but discrimination was poor (AUC=0.692; 95% CI 0.568-0.815). Conclusions A prediction model based on occupational health check variables does not identify non-sick listed workers at increased risk of LBP sick leave correctly. The model could be used to exclude the workers at the lowest risk on LBP sick leave from costly preventive interventions.